Halogen optical referred pulse-compression thermography for defect detection of CFRP

Shichun, Wu; Gao, Bin; Yang, Yang; Zhu, Yunqing; Burrascano, Pietro; Laureti, Stefano; Ricci, Marco; Wang, Yizhe

doi:10.1016/j.infrared.2019.103006

Cited by 38 publications

(16 citation statements)

References 61 publications

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“…A pulse-compression method was developed to detect defects with step heating thermography using halogen lamps [31]. Material defects were detected by convolving the acquired thermograms with a matched filter to estimate true impulse response.…”

Section: Overview Of Thermography Data Compressionmentioning

confidence: 99%

Compression of Pulsed Infrared Thermography Data With Unsupervised Learning for Nondestructive Evaluation of Additively Manufactured Metals

et al. 2022

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Additive manufacturing (AM) of high-strength metals, which is typically based on laser powder bed fusion (LPBF), can introduce microscopic pores in the AM metal. Pulsed Infrared Thermography (PIT) offers several advantages for nondestructive imaging of subsurface defects in AM structures because the method is one-sided, non-contact and scalable to structures of arbitrary size. However, high-resolution PIT imaging results in the generation of a large volume of thermography data (~TB), which creates challenges for the storage and transmission of data. Compression of thermography data requires an approach that achieves high data compression ratio while preserving weak thermal features corresponding to microscopic material defects. We investigate thermography data compression using several unsupervised learning (UL) algorithms, which include Principal Component Analysis, Independent Component Analysis, Exploratory Factor Analysis, Sparse Dictionary Learning, and a novel lightweight Thermography Compressive Sparse Autoencoder (TCSA). Algorithms are benchmarked using PIT experimental data obtained from imaging of a stainless steel plate with calibrated porosity defects imprinted with AM process. For all algorithms, we obtain compression ratio > 30 (highest compression of 46 is achieved with TCSA), and peak signal-to-noise ratio for reconstruction accuracy > 73dB. Compared to existing methods, advantages of UL algorithms include achieving high compression ratio while preserving weak features to allow extraction of microscopic material defects from images. UL-based methods have general applicability because they are adaptable to compression of different data types, and allow for memory-efficient training and rapid on-line augmentation of the model.

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Section: Overview Of Thermography Data Compressionmentioning

confidence: 99%

Compression of Pulsed Infrared Thermography Data With Unsupervised Learning for Nondestructive Evaluation of Additively Manufactured Metals

et al. 2022

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“…Research shows that there is an improvement of signal-to-noise ratio (SNR) of defect detection on CFRP composites using the PuCT method. One of the limitations of PuCT is the sideslope that influences the impulse response reconstruction (Wu et al, 2019). Moreover, very little research has been carried out on the impact defect on natural composites such as the flax composite.…”

Section: Impact and Lightning Defects In Composite Structure Materialsmentioning

confidence: 99%

Damage Identification on Impact and Lightning Damage of Flax Composite Laminates (Linum usitatissimum) Using Long-Pulse Thermography of a Low-Resolution Infrared Camera

et al. 2021

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The demand for composite fiber material is significantly high due to its excellent mechanical properties and its use in various industries. Recently, with the increasing awareness of environmental issues, researchers are now focusing more on eco-friendly and green materials. A biocomposite offers a good balance of strength and stiffness ratio, bending and membrane mechanical properties, balanced thermal distortion stability, reduced weight and cost, improved fatigue resistance, reduced notch sensitivity, and, comparatively, better performance than synthetic composites. Yet, due to the complex anisotropy of the composite material, the inspection and detection of inner defects become a challenge. Long-pulse thermography is one of the non-destructive evaluations (NDEs) used to detect defects in composite materials. However, very limited research has been carried out on the usage of a low-resolution infrared camera to perform defect or damage inspection on flax composite laminates. In this paper, an experimental setup of a long pulse thermography system using low-resolution infrared camera was performed on flax bio-composite to identify impact and lightning damage. The result highlights that with control parameters, a low-resolution infrared camera has the capability to capture the lightning and impact defects of flax biocomposites using the long-pulse thermography system. An image processing method is then applied to the defect to improve the quality of defect detection and reduce background noise.

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“…The results indicated that shallow defects (depth ≤ 2.11 mm) at the interfaces of CFRP plies can be detected using the PT method with the heat excitation of two halogen lamps. Furthermore, active IRTs were also applied in the defect detection in CSC structures in many recent studies such as Shardakov et al in 2016, Shi et al in 2019, Wu et al in 2019, and Lai et al in 2020 [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Thermography-Based Deterioration Detection in Concrete Bridge Girders Strengthened with Carbon Fiber-Reinforced Polymer

Mac

Huh

Doan

et al. 2020

Sensors

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In bridge structures worldwide, carbon fiber-reinforced polymer (CFRP) sheets are applied to strengthen weak components, especially concrete girders that are at a high risk of rapid degradation during the bridge’s operation owing to impacts from the superstructure’s weight and traffic loads. Regarding the thermography-based method (TM), although deteriorations in the concrete core are some of the main defects in concrete structures strengthened with CFRP, these do not receive as much attention as damage in the CFRP. Therefore, the interpretation of the structural health in terms of these defects using TM is still unclear. The problem presented in this work addresses the quantification of delamination inside the concrete part of a specimen with a CFRP sheet installed on the surface (assumed to be the girder surface strengthened with CFRP) via step heating thermography. Additionally, the empirical thermal diffusivity of concrete girders strengthened with a CFRP sheet (CSC girder), has not been provided previously, is proposed in the present study to predict delamination depths used for field investigations. Moreover, the effect of the CFRP sheet installed on the structure’s surface on the absolute contrast of delamination is clarified. Finally, advanced post-processing algorithms, i.e., thermal signal reconstruction and pulsed phase thermography, are applied to images obtained with step heating thermography to enhance the visibility of delamination in CSC girders.

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Halogen optical referred pulse-compression thermography for defect detection of CFRP

Cited by 38 publications

References 61 publications

Compression of Pulsed Infrared Thermography Data With Unsupervised Learning for Nondestructive Evaluation of Additively Manufactured Metals

Compression of Pulsed Infrared Thermography Data With Unsupervised Learning for Nondestructive Evaluation of Additively Manufactured Metals

Damage Identification on Impact and Lightning Damage of Flax Composite Laminates (Linum usitatissimum) Using Long-Pulse Thermography of a Low-Resolution Infrared Camera

Thermography-Based Deterioration Detection in Concrete Bridge Girders Strengthened with Carbon Fiber-Reinforced Polymer

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